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一种用于评估青少年新手驾驶员风险的贝叶斯有限混合变化点模型。

A Bayesian finite mixture change-point model for assessing the risk of novice teenage drivers.

作者信息

Li Qing, Guo Feng, Kim Inyoung, Klauer Sheila G, Simons-Morton Bruce G

机构信息

Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA.

Department of Statistics, Virginia Tech, Blacksburg, VA, USA.

出版信息

J Appl Stat. 2018;45(4):604-625. doi: 10.1080/02664763.2017.1288202. Epub 2017 Feb 10.

DOI:10.1080/02664763.2017.1288202
PMID:29375174
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5784858/
Abstract

The driving risk during the initial period after licensure for novice teenage drivers is typically the highest but decreases rapidly right after. The change-point of driving risk is a critical parameter for evaluating teenage driving risk, which also varies substantially among drivers. This paper presents latent class recurrent-event change-point models for detecting the change-points. The proposed model is applied to the Naturalist Teenage Driving Study, which continuously recorded the driving data of 42 novice teenage drivers for 18 months using advanced in-vehicle instrumentation. We propose a hierarchical BFMM to estimate the change-points by clusters of drivers with similar risk profiles. The model is based on a non-homogeneous Poisson process with piecewise-constant intensity functions. Latent variables which identify the membership of the subjects are used to detect potential clusters among subjects. Application to the Naturalistic Teenage Driving Study identifies three distinct clusters with change-points at 52.30, 108.99 and 150.20 hours of driving after first licensure, respectively. The overall intensity rate and the pattern of change also differ substantially among clusters. The results of this research provide more insight in teenagers' driving behaviour and will be critical to improve young drivers' safety education and parent management programs, as well as provide crucial reference for the GDL regulations to encourage safer driving.

摘要

新手青少年驾驶员在获得驾照后的初始阶段驾驶风险通常最高,但随后会迅速下降。驾驶风险的变化点是评估青少年驾驶风险的关键参数,不同驾驶员之间也存在很大差异。本文提出了用于检测变化点的潜在类别复发事件变化点模型。所提出的模型应用于“自然主义青少年驾驶研究”,该研究使用先进的车载仪器连续记录了42名新手青少年驾驶员18个月的驾驶数据。我们提出了一种分层贝叶斯有限混合模型,通过具有相似风险特征的驾驶员群体来估计变化点。该模型基于具有分段常数强度函数的非齐次泊松过程。用于识别受试者所属群体的潜在变量用于检测受试者之间的潜在聚类。应用于“自然主义青少年驾驶研究”识别出三个不同的聚类,首次获得驾照后驾驶52.30、108.99和150.20小时处有变化点。不同聚类之间的总体强度率和变化模式也有很大差异。本研究结果为青少年驾驶行为提供了更多见解,对于改进年轻驾驶员的安全教育和家长管理计划至关重要,同时也为鼓励更安全驾驶的分级驾照法规提供了关键参考。

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J Safety Res. 2015 Sep;54:49-54. doi: 10.1016/j.jsr.2015.06.013. Epub 2015 Jul 29.
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Higher crash and near-crash rates in teenaged drivers with lower cortisol response: an 18-month longitudinal, naturalistic study.青少年司机皮质醇反应较低时,碰撞和险些碰撞的比率更高:一项为期 18 个月的纵向、自然主义研究。
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Bayesian Hierarchical Poisson Regression Models: An Application to a Driving Study with Kinematic Events.贝叶斯分层泊松回归模型:在一项带有运动事件的驾驶研究中的应用。
J Am Stat Assoc. 2013;108(502):494-503. doi: 10.1080/01621459.2013.770702.
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